| import json | |
| from collections import Counter | |
| SAS = "" # e.g sp=r&st=... | |
| with open("canva.benchmark.json", "r") as f: | |
| data = json.load(f) | |
| print(""" | |
| <html> |
| from __future__ import print_function | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from torch.autograd import Variable | |
| def sample_gumbel(shape, eps=1e-20): | |
| U = torch.rand(shape).cuda() | |
| return -Variable(torch.log(-torch.log(U + eps) + eps)) |
| // Fast Block Distributed CUDA Implementation of the Hungarian Algorithm | |
| // | |
| // Annex to the paper: | |
| // Paulo A. C. Lopes, Satyendra Singh Yadav, Aleksandar Ilic, Sarat Kumar Patra , | |
| // "Fast Block Distributed CUDA Implementation of the Hungarian Algorithm", | |
| // Parallel Computing | |
| // | |
| // Hungarian algorithm: | |
| // (This algorithm was modified to result in an efficient GPU implementation, see paper) | |
| // |
| import math | |
| from visual import * | |
| # Data in units according to the International System of Units | |
| G = 6.67 * math.pow(10,-11) | |
| # Mass of the Earth | |
| ME = 5.973 * math.pow(10,24) | |
| # Mass of the Moon | |
| MM = 7.347 * math.pow(10,22) |
| import numpy as np | |
| import os, sys, getopt | |
| # Main path to your caffe installation | |
| caffe_root = '/path/to/your/caffe/' | |
| # Model prototxt file | |
| model_prototxt = caffe_root + 'models/bvlc_googlenet/deploy.prototxt' | |
| # Model caffemodel file |
People
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😉 :wink: |
😜 :stuck_out_tongue_winking_eye: |
😝 :stuck_out_tongue_closed_eyes: |
😀 :grinning: |
😗 :kissing: |
😙 :kissing_smiling_eyes: |
😛 :stuck_out_tongue: |
